CS 5854: Computational Systems Biology

CS 5854: Computational Systems Biology

Spring 2016, 11am-12:15pm, Tuesdays and Thursdays, Goodwin 244

About the Course

  • What is Computational Systems Biology?
  • What is the focus of this course?
  • Who should take this course?
  • Pre-requisites
  • Introductory Videos
  • Course structure
  • What is Computational Systems Biology?

    Cells, tissues, organs and organisms are systems of components whose interactions have been defined, refined, and optimised over hundreds of millions of years of evolution. Computational systems biology is a field that aims at a system-level understanding of biological systems by analysing biological data using computational techniques. Systems biology aims to answer the following key questions by integrating experimental and computational approaches:

    1. What are the basic structures and properties of the biological networks in a living cell?
    2. How does a biological system behave over time under various conditions?
    3. How does a biological system maintain its robustness and stability?
    4. How can we modify or construct biological systems to achieve desired properties?
    Answers to these questions require breakthroughs in the fields of biology, chemistry, computer science, engineering, mathematics and other fields together with an evolution of our educational structures. The explosive progress of genome sequencing projects and the massive amounts of data that high-throughput experiments in DNA microarrays, proteomics, and metabolomics yield drive advances in this field. Sophisticated computational ideas process these data sources in an effort to systematically analyse and unravel the complex biological phenomena that take place in a cell.

    What is the focus of this course?

    As mentioned above, cell and molecular biology are awash in data. Over the last 10-15 years, numerous computational methods have been developed that can assimilate and integrate massive quantities of data in order to find hidden patterns in them that may contain useful biological information. However, manually-intensive examination of the results is necessary in order to propose hypotheses that can be validated in the wet lab. This drawback mitigates the utility of these methods for driving biological experiments. A more recent trend is the development of methods that have been designed from the outset to more easily suggest experimental hypotheses and validation studies. This course will discuss such methods, mainly developed over the last three-four years, by studying papers from the literature.

    Who should take this course?

    You should take this course if you are curious to find out how the latest research is shaping our understanding of how the living cell behaves as a system. The course will cover the latest research in computational systems biology, primarily in the context of molecular interaction networks. We will spend a significant part of the course on examining how the analysis of DNA microarray data and other high-throughput data is crucial to progress in this area. The course is geared towards graduate students whose main research interest is bioinformatics or who use bioinformatic tools and techniques in their research.

    There are many exciting and profound issues that researchers in this area are actively investigating, such as the robustness of biological systems, network structures and dynamics, and applications to drug discovery. During this course, we will come across many interesting open research problems. Taking this course might be an excellent way to create research topics and projects for your Master's or Ph.D. thesis in the area of bioinformatics/computational biology. In this course, you will be able to communicate and work with students and researchers with varied backgrounds. In addition, Virginia Tech is humming with research activities in this area.

    Pre-requisites

    The course is open to students with graduate standing. I hope that both students with computational backgrounds and students with experience in the life sciences will take this course. If you find this course interesting but are not sure whether your background matches the pre-requisites, please talk to me.

    Computer Science graduate students: the Data and Algorithm Analysis (CS 4104) or similar course is a pre-requisite. It will help if you also have taken Algorithms in Bioinformatics (CS 5124) and a course on combinatorics and graph theory such as Applied Combinatorics (MATH 3134). An introductory molecular biology course such as Biological Paradigms for Bioinformatics will provide extremely useful biological background.

    Life science graduate students: I expect that you have taken courses in biochemistry, cell biology, and molecular biology. A course like Computation for Life Sciences (CS 5045) provides very useful computational background.

    Introductory Videos

    For students with computational backgrounds, I have listed some videos below that provide introductions into molecular and cell biology.

    Course structure

    The course will primarily be driven by lectures and by seminars where one or more students present a related group of papers from literature. I will try to arrange papers that cover both biological and computational aspects. Ideally, I would like a group to contain students with backgrounds in computer science, mathematics, and/or statistics and students with backgrounds in biology and chemistry.

    Your grade will depend on your presentation (20%), on class participation (30%), and a final project (50%). The final project is a group software project. I will define software projects that are inspired by the papers you present in class. The project will involve creating some new software or using existing software innovatively combined with some intensive biological analysis of the results. You are welcome to suggest a project to me.

Table 1: Schedule (subject to change throughout the semester). Links in the "Topic and Papers" column point to specific papers assigned for each class. Links in "Presenter" column point to the slides for the lecture.
Date Topic and Papers Presenter(s)
Jan 19, 2016 Introduction to Computational Systems Biology T. M. Murali
Jan 21, 2016 Introduction to Computational Systems Biology, Discussion of papers T. M. Murali
Jan 26, 2016 Exploring human GO annotations, Course Projects T. M. Murali
Jan 28, 2016 Directed or undirected? Course Projects, same lecture as previous class T. M. Murali
Feb 2, 2016 Course Projects, same lecture as previous class T. M. Murali
Feb 4, 2016 Pathways on Demand: Automatic Reconstruction of Human Signaling Networks T. M. Murali
Feb 9, 2016 Pathways on Demand: Automatic Reconstruction of Human Signaling Networks T. M. Murali
Feb 11, 2016 Pathways on Demand: Automatic Reconstruction of Human Signaling Networks T. M. Murali
Feb 16, 2016 No class (meeting with project group)  
Feb 18, 2016 Fundamentals of protein interaction network mapping Sajal Dash and Rebekah Less
Feb 23, 2016 Fundamentals of protein interaction network mapping (Contd) Sajal Dash and Rebekah Less
Feb 23, 2016 XTalk: a path-based approach for identifying crosstalk between signaling pathways Sophia Orbach and Phillip Summers
Feb 25, 2016 XTalk: a path-based approach for identifying crosstalk between signaling pathways Sophia Orbach and Phillip Summers
Mar 1, 2016 XTalk: a path-based approach for identifying crosstalk between signaling pathways Sophia Orbach and Phillip Summers
Mar 3, 2016 XTalk: a path-based approach for identifying crosstalk between signaling pathways Sophia Orbach and Phillip Summers
Mar 8, 2016 No class (Spring break)  
Mar 10, 2016 No class (Spring break)  
Mar 15, 2016 Midterm project reviews  
Mar 17, 2016 Discovering pathways by orienting edges in protein interaction networks Amogh Jalihal and Prathyush Sambataru
Mar 22, 2016 Network orientation via shortest paths Amogh Jalihal and Prathyush Sambataru
Mar 24, 2016 Class cancelled  
Mar 29, 2016 Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes (HotNet2) Jeff Law and Bronson Weston
Mar 31, 2016 Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes (HotNet2) Jeff Law and Bronson Weston
Apr 5, 2016 Linking the signaling cascades and dynamic regulatory networks controlling stress responses Aditya Bharadwaj and Daniel Chen
Apr 7, 2016 Network-Based Integration of Disparate Omic Data To Identify "Silent Players" in Cancer Aditya Bharadwaj and Daniel Chen
Apr 12, 2016 Discovering causal pathways linking genomic events to transcriptional states using Tied Diffusion Through Interacting Events Jiyoung Lee and Aditya Pratapa
Apr 14, 2016 Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling Jiyoung Lee and Aditya Pratapa
Apr 19, 2016 Class cancelled  
Apr 21, 2016 Kinome-wide Decoding of Network-Attacking Mutations Rewiring Cancer Signaling Jiyoung Lee and Aditya Pratapa
Apr 21, 2016 Targeted exploration and analysis of large cross-platform human transcriptomic compendia T. C. Jones and Xioanan Fu
Apr 26, 2016 Targeted exploration and analysis of large cross-platform human transcriptomic compendia T. C. Jones and Xioanan Fu
Apr 28, 2016 Understanding multicellular function and disease with human tissue-specific networks Brittany Boribong and Alex Song
May 3, 2016 Understanding multicellular function and disease with human tissue-specific networks Brittany Boribong and Alex Song
May 5, 2016 Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration Sajal Dash and Rebekah Less
May ??, 2016 Final project presentations  
May 6, 2016 Final project reports, due by 5pm  

Author: "T. M. Murali"

Created: 2016-04-15 Fri 13:14